PubMed2026
The European Thoracic Oncology Platform (ETOP) International Breast Cancer Study Group (IBCSG) Partners Foundation initiated a series of workshops for experts to review current evidence and offer recommendations to guide future antibody-drug conjugate (ADC) research. Here, we summarise key findings from the third workshop, which included experts in various solid tumours, basic/translational research scientists and pharmaceutical industry representatives. Recent positive phase III trial data have further incorporated ADCs into the standard of care [e.g. lung: sacituzumab tirumotecan; breast: trastuzumab deruxtecan (T-DXd), sacituzumab govitecan, datopotamab deruxtecan; muscle-invasive bladder cancer: enfortumab vedotin; ovarian cancer: mirvetuximab soravastine; cervical cancer: tisotumab vedotin]. Thus, research priorities must be tailored according to tumour type, potentially focussing initially on settings where ADCs could replace chemotherapy. Many phase III ADC trials have been initiated based on positive phase I data and although these trials are larger than those conducted historically, prespecified criteria (e.g. patient numbers and magnitude of efficacy) should be met to justify proceeding directly to phase III. Importantly, although several ADCs have been successfully developed without mandatory biomarker selection, biomarker-driven ADC development enables rational patient selection, as illustrated by multiple ADCs (e.g. T-DXd, mirvetuximab soravtansine and telisotuzumab vedotin). The identification, development and validation of predictive biomarkers are therefore essential, particularly given several critical nuances, including the algorithms used to assess biomarker status and the type of specimen analysed, all of which may be influenced by temporal and spatial heterogeneity. Additional ADC research priorities include the optimisation of ADC constructs to enhance efficacy/tolerability and the identification of reliable ADC targets, including work to elucidate attributes of already-identified targets. Finally, considering the vast amount of ADC-related data being generated, artificial intelligence could be leveraged to analyse combined datasets and generate composite biomarkers, including tumour histology, optimal target expression thresholds, molecular alterations and activated pathways affecting payload activity and target function, to accelerate research.